org.neuroph.nnet.learning.kmeans

Class KVector

java.lang.Object

org.neuroph.nnet.learning.kmeans.KVector

public class KVector
extends Object

Represents feature vector used in k-means clustering algorithm

Author:

Zoran Sevarac, Uros Stojkic

Constructor Summary

Constructors

Constructor and Description
KVector(double[] values)
KVector(int size)

Method Summary

Modifier and Type	Method and Description
double	distanceFrom(KVector otherVector) Calculates and returns euclidean distance of this vector from the given cluster
Cluster	getCluster()
double	getDistance()
double	getIntensity() Calculates and returns intensity of this vector
double	getValueAt(int idx)
double[]	getValues()
void	setCluster(Cluster cluster)
void	setDistance(double distance)
void	setValueAt(int idx, double value)
void	setValues(double[] values)
int	size()
String	toString()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

KVector

public KVector(int size)

KVector

public KVector(double[] values)

Method Detail

setValueAt

public void setValueAt(int idx,
                       double value)

getValueAt

public double getValueAt(int idx)

getValues

public double[] getValues()

setValues

public void setValues(double[] values)

getCluster

public Cluster getCluster()

setCluster

public void setCluster(Cluster cluster)

getIntensity

public double getIntensity()

Calculates and returns intensity of this vector

Returns:

intensity of this vector

distanceFrom

public double distanceFrom(KVector otherVector)

Calculates and returns euclidean distance of this vector from the given cluster

Parameters:

cluster -

Returns:

euclidean distance of this vector from given cluster

size

public int size()

toString

public String toString()

Overrides:

toString in class Object

getDistance

public double getDistance()

setDistance

public void setDistance(double distance)